fix: support chunk-prefill and fix bug in check_stop

vllm/zero_overhead/v1/core.py

@@ -14,12 +14,13 @@ requsets_valid_token_len = {}
 def check_stop(request: Request,
                max_model_len: int,
                pooler_output: Optional[torch.Tensor] = None,
-               use_valid_token_len:bool = False) -> bool:
+               use_valid_token_len: bool = False,
+               last_token_offset: Optional[int] = 0) -> bool:
     if use_valid_token_len:
         if request.request_id not in requsets_valid_token_len:
             requsets_valid_token_len[request.request_id] = 0
             return False
-        valid_output_len = requsets_valid_token_len[request.request_id]
+        valid_output_len = requsets_valid_token_len[request.request_id] - last_token_offset
     else:
         valid_output_len = request.num_output_tokens
     valid_num_tokens = request.num_prompt_tokens + valid_output_len
@@ -100,7 +101,8 @@ def zero_overhead_update_from_output(scheduler:Scheduler,
             # Check for stop and update request state.
             # This must be called before we make the EngineCoreOutput.
             for num_new, output_token_id in enumerate(new_token_ids, 1):
-                stopped = check_stop(request, scheduler.max_model_len, True)
+                stopped = check_stop(request, scheduler.max_model_len, use_valid_token_len=True,
+                                     last_token_offset=len(new_token_ids) - num_new)
                 if stopped:
                     kv_transfer_params = scheduler._free_request(request)
                     del new_token_ids[num_new:]  # Trim new tokens if needed.

vllm/zero_overhead/v1/gpu_model_runner.py

@@ -38,29 +38,22 @@ def fused_last_valid_scatter_kernel(
     BLOCK_T: tl.constexpr,
 ):
     pid = tl.program_id(0)
-
     # indices
     req_idx = tl.load(update_req_ptr + pid)
     input_pos = tl.load(input_pos_ptr + pid)
-
     # load row
     offs = tl.arange(0, BLOCK_T)
     mask = offs < T
-
     row_ptr = last_ids_ptr + req_idx * stride0 + offs * stride1
     vals = tl.load(row_ptr, mask=mask, other=-1)
-
-    # ✅ 正确做法：index reduction
     idx = tl.where(vals != -1, offs, -1)
     last_idx = tl.max(idx, axis=0)
-
     # load last token
     last_val = tl.load(
         last_ids_ptr + req_idx * stride0 + last_idx * stride1,
         mask=last_idx >= 0,
         other=0,
     )
-
     # scatter
     tl.store(input_ids_ptr + input_pos, last_val)
 
@@ -138,9 +131,6 @@ class V1ZeroModelRunner(GPUModelRunner):
                     )
 
             for req_idx, start_idx, end_idx in self.token_ids_cpu_fix_record:
-                if start_idx == -1:
-                    self.fix_sampled_token_ids[req_idx].clear()
-                else:
                 num_accepted_tokens = len(self.fix_sampled_token_ids[req_idx])
                 req_id = self.fix_req_ids[req_idx]
                 if req_id in self.input_batch.req_ids:
@@ -779,6 +769,31 @@ class V1ZeroModelRunner(GPUModelRunner):
         for i in discard_sampled_tokens_req_indices:
             valid_sampled_token_ids[i].clear()
 
+        if not self.speculative_config:
+            # Speculative decoding is not enabled.
+            spec_token_ids = None
+            fix_draft_req_ids = None
+        else:
+            if self.last_draft_host_tokens is not None:
+                self.last_draft_event.synchronize()
+                fix_draft_token_ids = self.last_draft_host_tokens.tolist()
+
+            mask = (sampled_token_ids == -1)
+            mask_int = mask.int()
+            first_neg_one_indices = torch.argmax(mask_int, dim=1)
+            num_accepted_tokens_tensor = torch.where(torch.any(mask, dim=1), first_neg_one_indices, sampled_token_ids.size(1)) - 1
+            spec_token_ids = self.propose_draft_token_ids(
+                scheduler_output,
+                num_accepted_tokens_tensor,
+                sampled_token_ids,
+                sampling_metadata,
+                hidden_states,
+                sample_hidden_states,
+                aux_hidden_states,
+                spec_decode_metadata,
+                attn_metadata,
+            )
+
         # Cache the sampled tokens in the model runner, so that the scheduler
         # doesn't need to send them back.
         # NOTE(woosuk): As an exception, when using PP, the scheduler sends
@@ -789,12 +804,9 @@ class V1ZeroModelRunner(GPUModelRunner):
         self.last_sampled_token_lens = []
         for req_idx, sampled_ids in enumerate(valid_sampled_token_ids):
             req_id = self.input_batch.req_ids[req_idx]
-            self.last_sampled_req_ids.append(req_id)
-            cache_output_len = -1
             if not sampled_ids:
-                self.last_sampled_token_lens.append(-1)
-                self.token_ids_cpu_fix_record.append([req_idx, -1, -1])
                 continue
+            self.last_sampled_req_ids.append(req_id)
 
             start_idx = self.input_batch.num_tokens_no_spec[req_idx]
             end_idx = start_idx + len(sampled_ids)
@@ -809,32 +821,6 @@ class V1ZeroModelRunner(GPUModelRunner):
             self.input_batch.num_tokens_no_spec[req_idx] = end_idx
             self.input_batch.num_tokens[req_idx] = end_idx
 
-        if not self.speculative_config:
-            # Speculative decoding is not enabled.
-            spec_token_ids = None
-            fix_draft_req_ids = None
-        else:
-            sampled_token_ids_cpu = sampled_token_ids.to('cpu', non_blocking=True)
-            if self.last_draft_host_tokens is not None:
-                self.last_draft_event.synchronize()
-                fix_draft_token_ids = self.last_draft_host_tokens.tolist()
-
-            mask = (sampled_token_ids == -1)
-            mask_int = mask.int()
-            first_neg_one_indices = torch.argmax(mask_int, dim=1)
-            num_accepted_tokens_tensor = torch.where(torch.any(mask, dim=1), first_neg_one_indices, sampled_token_ids.size(1)) - 1
-            spec_token_ids = self.propose_draft_token_ids(
-                scheduler_output,
-                num_accepted_tokens_tensor,
-                sampled_token_ids,
-                sampling_metadata,
-                hidden_states,
-                sample_hidden_states,
-                aux_hidden_states,
-                spec_decode_metadata,
-                attn_metadata,
-            )
-
         # Clear KVConnector state after all KVs are generated.
         if has_kv_transfer_group():
             get_kv_transfer_group().clear_connector_metadata()